pdmpBorder-class: Class pdmpBorder

In CharlotteJana/pdmpsim: Simulate Piecewise Deterministic Markov Processes

Description

An S4 class to represent a piecewise deterministic markov process with Borders (PDMP).
This class is based on the pdmpsim which itselve is based on simecol Package and provides a possibility to simulate piecewise deterministic markov processes with borders. These processes are described in [Zei09] and [Ben+15]. It's closed to the original definition given in [Dav84], but one Restriction left: The number of continous variables should be independent of the state (of the discrete variable) of the process. The class is based on the pdmpModel class of package pdmpsim but introduces additional slots

Usage

pdmpBorder(obj = NULL, descr = character(0), dynfunc, jumpfunc,
  ratefunc, borderfunc = function(t, z, parms) z, borroot = function(t,
  z, parms) NULL, terroot = function(t, z, parms) NULL, times = c(from
  = 0, to = 10, by = 1), init = c(0, 0), parms = c(0),
  discStates = list(0), out = NULL, solver = "lsodar",
  initfunc = NULL)

Arguments

borderfunc	a function(t, x, parms) that returns the new state after reaching a borroot.
borroot	a function(t, x, parms) that returns a zero for each continous variable reaching a border where the process forces a jump (maximum).
terroot	a function(t, x, parms) that returns a zero for each contionous variable reaching a border where the process forces a termination (minimum).

Slots

borderfunc

a function(t, x, parms) that returns the new state after reaching a borroot.

borroot

a function(t, x, parms) that returns a zero for each continous variable reaching a border where the process forces a jump (maximum).

terroot

a function(t, x, parms) that returns a zero for each contionous variable reaching a border where the process forces a termination (minimum).

References

[Dav84]	Davis, M. H. (1984). Piecewise-deterministic Markov processes: A general class of
	non-diffusion stochastic models. Journal of the Royal Statistical Society. Series B
	(Methodological), 353-388.
[Zei09]	S. Zeiser. Classical and Hybrid Modeling of Gene Regulatory Networks. 2009.
[Ben+15]	Benaïm, M., Le Borgne, S., Malrieu, F., & Zitt, P. A. (2015). Qualitative properties
	of certain piecewise deterministic Markov processes. In Annales de l'Institut Henri
	Poincaré, Probabilités et Statistiques (Vol. 51, No. 3, pp. 1040-1075). Institut
	Henri Poincaré.

See Also

Class pdmpModel provides a method sim for simulation, pdmpBorder-accessors{accessor functions}

CharlotteJana/pdmpsim documentation built on July 2, 2019, 5:37 a.m.